Bibliographic record
Abstract
The purpose of the research is to define the factors that negatively affect education and learning process. Descriptive content analysis, one of the non-interactive qualitative research designs, was used to analyze the data. The analyses were conducted in six stages. First, aim, subject, and research questions were determined. Literature review was done according to the inclusion and exclusion criteria, the literature was read, the literature tags were created in the form of a table, the codes, categories, themes were created inductively according to the descriptive content analysis, and finally, analysis, association, interpretation, signification, and reporting were made. To this aim, 238 research conducted between 2014 and 2018 were jointly investigated within the framework of determined criteria. Correlation between raters was determined as rp= 0.94. According to the obtained results, variables that negatively affect learning related to technology and media may be indicated as phone, tablet, computer, game, internet, cartoons, social media, television, and TV series. Private teaching institutions and central examinations that negatively affect teaching are among the variables related to exams. Negative and disruptive factors arising from the school, education system, and educational practices; assignments, disconnection from real life, discipline problems, legislation and procedures, teaching practices that do not change or be updated, and a low possibility for failing a class are educational fashions. Addiction related viruses such as drugs, technology addiction, smoking habits affect education negatively. Obesity and excessive consumption culture and unhealthy nutrition problems that are health-related problems are also observed. Violence, swearing, using slang words, peer bullying, moral collapse, noise pollution, and problems stemming from ignoring others are the problems arising from all kinds of school environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".